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Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions.

, , and . Neural Computation, 16 (2): 223-250 (2004)

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Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions., , and . Neural Computation, 16 (2): 223-250 (2004)Better than least squares: comparison of objective functions for estimating linear-nonlinear models.. NIPS, page 1305-1312. Curran Associates, Inc., (2007)Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals., , and . NIPS, page 261-268. MIT Press, (2002)Quantifying Information Conveyed by Large Neuronal Populations., and . Neural Computation, 31 (6): 1015-1047 (2019)Characterizing Responses of Translation-Invariant Neurons to Natural Stimuli: Maximally Informative Invariant Dimensions., , , and . Neural Computation, 24 (9): 2384-2421 (2012)Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli., , , and . PLoS Computational Biology, (2013)A Robust Feedforward Model of the Olfactory System., and . PLoS Computational Biology, (2016)Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models., , , and . PLoS Computational Biology, (2011)Contextual modulation of V1 receptive fields depends on their spatial symmetry., and . Journal of Computational Neuroscience, 26 (2): 203-218 (2009)Double-Gabor Filters Are Independent Components of Small Translation-Invariant Image Patches., , and . Neural Computation, 25 (4): 922-939 (2013)